Mathematical Fundamentals for Data Science

About This Course

I. Rationale:

Machine learning, which is a part of data science, uses tools and results from various mathematical disciplines, including (but not limited to): linear algebra, probability, statistics, multivariable calculus, information theory, and computational learning theory. Familiarity with these ideas is crucial in better understanding the underlying idea of machine learning algorithms and perform the analysis on the results derived from them. However, although many online and offline courses for data science, machine learning, and artificial intelligence are available, only a limited number of courses offering the mathematical background for deep understanding of machine learning models and algorithms is available. In response, this course is designed to fill these gaps and through this course, we attempt to provide some basic mathematical background needed for introductory classes in machine learning.

In this course, we specifically cover basic principles in linear algebra, probability and statistics, and multivariable calculus and optimization, which are widely used and needed in machine learning.

We remark that this course concerns the mathematical background for data science and machine learning, not machine learning itself. That is, although it is possible to highlight the relevance of a mathematical concept with a machine learning algorithm, we will not discuss a specific machine learning models or algorithms.

II. Course Aims and Outcomes:

Aims :
The aim of this course is to provide students the basic mathematical background necessary to understand machine learning models or algorithms.

Specific Learning Outcomes:
By the end of this course, students will:
• Better understand the mathematical terms, concepts and algebra rules that data scientists should know before moving on to the higher level in the machine learning study

III. Course Requirements:

We presume students have a basic understanding of a high-school level mathematics although this course is designed for people who might be familiar with and exposed to a certain level of basic concepts in probability, calculus or linear algebra at the high-school or 1st-year college level.

IV. Grading Procedures:

Grading policy is as follows:

• Quizzes (60%): The course has XX quizzes.

• Projects (20%)

• Final Exam (20%): There will be a final exam one week after the last lecture.

To pass the course, you must score 60% or above.

V. Course Schedule

• Course registration period : 2017.10.1~2017.11.30

• Opening and Ending : 2017.10.10~2018. 2.10

Ⅵ. Course Staff

Taesu Cheong

Professor, School of Industrial Management Engineering, Korea University